Analyse der Matoma-HaNS-Daten

1 Hintergrund

Dieser Arbeitsbericht schildert das technische Vorgehen im Rahmen der Analyse der Matomo-Daten des BMBF-Projekt “HaNS”.

1.1 Vorgehen

Die Matomo-Klickdaten aller Semester der Projektlaufzeit wurden für diese Analyse verarbeitet. Mit Hilfe einer R-Pipeline wurden eine Reihe von Forschungsfragen analysiert.

Der komplette Code ist online dokumentiert unter https://github.com/sebastiansauer/hans. Aus Datenschutzgründen sind online keine Daten eingestellt.

Die zentrale Analyse-Pipeline-Datei ist https://github.com/sebastiansauer/hans/blob/main/_targets.R.

1.2 Forschungsfragen

1. Wie viele Nutzer gibt es und in welchem Zeitraum?
2. In welcher Frequenz wird HaNS aufgesucht? Wie groß sind die zeitlichen Zwischenräume zwischen der Benutzung der Plattform?
3. Wie oft wird HaNS pro Zeitraum (z.B. Monat) besucht?
4. Wie verändert sich die Nutzung im Zeitverlauf?
5. Wie viele Aktionen bringt ein Visit mit sich? Wie ist die statistische Verteilung der Aktionen pro Visit?
6. Wie lang verweilen die Nutzer pro Visit?
7. Wie verändert sich die Nutzungsdauer pro Visit im Zeitverlauf?
8. Welche Aktionen führen die Nutzer auf Hans aus?
9. Wie verändern sich die Verteilungen der Aktionshäufigkeiten im Zeitverlauf?
10. An welchen Tagen und zu welcher Zeit kommen die User zu HaNS?
11. Wie häufig und in welcher Art inteagieren die Nutzer mit dem LLM in HaNS?
12. Wie groß ist der Anteil der Nutzer, die mit dem LLM interagieren?
13. Wie verändert sich der Anteil der Nutzer, die mit dem LLM interagieren, im Zeitverlauf?
14. Wie oft wird auf ein Wort im Transkript des LLM geklickt?
15. Wie oft wird ein Transkript-Dienst in HaNS in Anspruch genommen?
16. Wie verändert sich die Nutzung der Transkript-Dienste in HaNS im Zeitverlauf?
17. Wie lange werden Videos angeschaut?
18. Wie verändert sich die Betrachtungsdauer im Zeitverlauf?

2 Setup

2.1 R-Pakete starten

library(targets)
library(tidyverse)
library(ggokabeito)
library(easystats)
library(gt)
library(ggfittext)
library(scales)
library(visdat)
library(collapse)
library(ggpubr)
library(knitr)

theme_set(theme_minimal())

2.2 Optionen setzen

options(lubridate.week.start = 1)  # Monday as first day
#options(collapse_mask = "all") # use collapse for all dplyr operations
options(chromote.headless = "new")  # Chrome headleass needed for gtsave

Immer knitr::kable für das Zeigen von Data frames verwenden:

3 Datenaufbereitung und Analysepipeline

3.1 Targets-Pipeline stellt Überblick aller Analyseschritte dar

Die Analyse wird im Rahmen einer Targets-Pipeline beschrieben und ist offen auf Github einsehbar.

3.2 Langformat

Aufgrund des “rechts flatternden” Datenformat (d.h. unterschiedliche Zeilenlängen) wurden die Daten in ein Langformat überführt, zwecks besserer/einfacherer Analyse.

Dazu wurden (neben den ID-Variablen, v.a. idvisit) die actionDetails_-Variablen verwendet. Der Code des Pivotierens in das Langformat ist in der Funktion longify-data.R einsehbar.

Die Daten im Langformat wurden dann noch etwas aufbereitet mt der Funktion slimify-data.R.

tar_load(data_slim_filtered_head)

data_slim_filtered_head |> 
  head(30) |> 
  kable()

nr	type	value	idvisit
0	subtitle	https://hans.th-nuernberg.de/login	1
0	subtitle	https://hans.th-nuernberg.de/login?evalId=none&user=undefined&role=undefined	1
0	subtitle	https://hans.th-nuernberg.de/	1
0	subtitle	https://hans.th-nuernberg.de/login?evalId=none&role=undefined	1
0	timestamp	2023-03-23 14:20:12	1
0	timestamp	2024-06-06 11:19:16	1
0	timestamp	2022-12-05 15:33:45	1
0	timestamp	2023-10-04 16:19:46	1
1	eventcategory	click_videocard	1
1	eventcategory	login	1
1	eventaction	Einführung in HAnS	1
1	eventaction	success	1
1	timestamp	2023-03-23 14:20:26	1
1	timestamp	2024-06-06 11:21:07	1
1	timestamp	2022-12-05 15:33:49	1
1	timestamp	2023-10-04 16:19:54	1
1	subtitle	https://hans.th-nuernberg.de/	1
1	subtitle	https://hans.th-nuernberg.de/login?evalId=none&user=undefined&role=undefined	1
1	subtitle	Category: ““click_videocard’, Action:”“Einführung in HAnS””	1
1	subtitle	Category: ““login’, Action:”“success””	1
2	subtitle	Category: ““click_toggle’, Action:”“Evaluation””	1
2	subtitle	https://hans.th-nuernberg.de/video-player?uuid=b57c1dfe-a667-48a6-b43d-dac7e600ae8c	1
2	subtitle	https://hans.th-nuernberg.de/?evalId=none&role=developer	1
2	timestamp	2023-03-23 14:20:28	1
2	timestamp	2022-12-05 15:33:49	1
2	timestamp	2023-10-04 16:19:54	1
2	eventcategory	click_toggle	1
2	eventaction	Evaluation	1
3	eventcategory	click_button	1
3	eventcategory	videoplayer_click	1

4 Überblick über die Daten

4.1 Roh-Daten laden und inspizieren (data_all_fact)

tar_load(data_all_fct)

4.1.1 Dimension

Der Roh-Datensatz verfügt über

• 19086 Zeilen
• 8399 Spalten (Dubletten und Spalten mit Bildern bereits entfernt)

Jede Zeile entspricht einem “Visit”.

4.1.2 Erster Blick

data_all_fct_head100 <- 
data_all_fct %>% 
  select(1:100) %>% 
  slice_head(n = 100) 

data_all_fct_head100 %>% 
  visdat::vis_dat()

4.1.3 Namen (1-100)

data_all_fct_head100 %>% 
  names()

  [1] "file_id"                                 
  [2] "idvisit"                                 
  [3] "visitip"                                 
  [4] "visitorid"                               
  [5] "fingerprint"                             
  [6] "actiondetails_0_type"                    
  [7] "actiondetails_0_url"                     
  [8] "actiondetails_0_pageidaction"            
  [9] "actiondetails_0_idpageview"              
 [10] "actiondetails_0_servertimepretty"        
 [11] "actiondetails_0_pageid"                  
 [12] "actiondetails_0_sitesearchkeyword"       
 [13] "actiondetails_0_sitesearchcount"         
 [14] "actiondetails_0_pageviewposition"        
 [15] "actiondetails_0_title"                   
 [16] "actiondetails_0_subtitle"                
 [17] "actiondetails_0_timestamp"               
 [18] "sitecurrency"                            
 [19] "sitecurrencysymbol"                      
 [20] "serverdate"                              
 [21] "visitserverhour"                         
 [22] "lastactiontimestamp"                     
 [23] "lastactiondatetime"                      
 [24] "servertimestamp"                         
 [25] "firstactiontimestamp"                    
 [26] "servertimepretty"                        
 [27] "serverdatepretty"                        
 [28] "serverdateprettyfirstaction"             
 [29] "servertimeprettyfirstaction"             
 [30] "visitortype"                             
 [31] "visitcount"                              
 [32] "dayssincefirstvisit"                     
 [33] "secondssincefirstvisit"                  
 [34] "visitduration"                           
 [35] "visitdurationpretty"                     
 [36] "searches"                                
 [37] "actions"                                 
 [38] "interactions"                            
 [39] "referrertype"                            
 [40] "referrertypename"                        
 [41] "referrername"                            
 [42] "referrerurl"                             
 [43] "referrersearchengineurl"                 
 [44] "languagecode"                            
 [45] "language"                                
 [46] "devicetype"                              
 [47] "devicebrand"                             
 [48] "devicemodel"                             
 [49] "operatingsystem"                         
 [50] "operatingsystemname"                     
 [51] "operatingsystemcode"                     
 [52] "operatingsystemversion"                  
 [53] "browserfamily"                           
 [54] "browserfamilydescription"                
 [55] "browser"                                 
 [56] "browsername"                             
 [57] "browsercode"                             
 [58] "browserversion"                          
 [59] "events"                                  
 [60] "continent"                               
 [61] "continentcode"                           
 [62] "country"                                 
 [63] "countrycode"                             
 [64] "countryflag"                             
 [65] "region"                                  
 [66] "regioncode"                              
 [67] "city"                                    
 [68] "location"                                
 [69] "latitude"                                
 [70] "longitude"                               
 [71] "visitlocaltime"                          
 [72] "visitlocalhour"                          
 [73] "dayssincelastvisit"                      
 [74] "secondssincelastvisit"                   
 [75] "resolution"                              
 [76] "plugins"                                 
 [77] "provider"                                
 [78] "providername"                            
 [79] "providerurl"                             
 [80] "actiondetails_0_pageloadtime"            
 [81] "actiondetails_0_timespent"               
 [82] "actiondetails_0_timespentpretty"         
 [83] "actiondetails_0_pageloadtimemilliseconds"
 [84] "actiondetails_1_type"                    
 [85] "actiondetails_1_url"                     
 [86] "actiondetails_1_pageidaction"            
 [87] "actiondetails_1_idpageview"              
 [88] "actiondetails_1_servertimepretty"        
 [89] "actiondetails_1_pageid"                  
 [90] "actiondetails_1_eventcategory"           
 [91] "actiondetails_1_eventaction"             
 [92] "actiondetails_1_pageviewposition"        
 [93] "actiondetails_1_timestamp"               
 [94] "actiondetails_1_title"                   
 [95] "actiondetails_1_subtitle"                
 [96] "actiondetails_2_type"                    
 [97] "actiondetails_2_url"                     
 [98] "actiondetails_2_pageidaction"            
 [99] "actiondetails_2_idpageview"              
[100] "actiondetails_2_servertimepretty"        

4.1.4 Werte der erst 100 Spalten

data_all_fct_head100 %>% 
  glimpse()

Rows: 100
Columns: 100
$ file_id                                  <fct> matomo_export_2023-03-23.csv,…
$ idvisit                                  <fct> 17, 16, 15, 13, 14, 11, 10, 1…
$ visitip                                  <fct> 87.150.0.0, 217.84.0.0, 90.18…
$ visitorid                                <fct> 9b735f0eb17af6a8, c77ae8b840d…
$ fingerprint                              <fct> 86408e41c606f8f7, 0584640e01c…
$ actiondetails_0_type                     <fct> search, action, action, actio…
$ actiondetails_0_url                      <fct> NA, https://hans.th-nuernberg…
$ actiondetails_0_pageidaction             <fct> NA, 2, 2, 3, 2, 9, 3, 4, 2, 4…
$ actiondetails_0_idpageview               <fct> NA, PlZbGn, 0f7b07, A6owU2, h…
$ actiondetails_0_servertimepretty         <fct> "Mar 23, 2023 21:18:53", "Mar…
$ actiondetails_0_pageid                   <fct> 325, 324, 323, 164, 176, 145,…
$ actiondetails_0_sitesearchkeyword        <fct> "GDI", NA, NA, NA, NA, NA, NA…
$ actiondetails_0_sitesearchcount          <fct> 0, NA, NA, NA, NA, NA, NA, NA…
$ actiondetails_0_pageviewposition         <fct> 1, 1, 1, 1, 1, 1, 1, NA, 1, N…
$ actiondetails_0_title                    <fct> Site Search, HAnS, HAnS, HAnS…
$ actiondetails_0_subtitle                 <fct> "GDI", "https://hans.th-nuern…
$ actiondetails_0_timestamp                <fct> 2023-03-23 21:18:53, 2023-03-…
$ sitecurrency                             <fct> USD, USD, USD, USD, USD, USD,…
$ sitecurrencysymbol                       <fct> $, $, $, $, $, $, $, $, $, $,…
$ serverdate                               <fct> 2023-03-23, 2023-03-23, 2023-…
$ visitserverhour                          <fct> 20, 20, 18, 18, 17, 17, 16, 1…
$ lastactiontimestamp                      <fct> 2023-03-23 20:18:53, 2023-03-…
$ lastactiondatetime                       <fct> 2023-03-23 20:18:53, 2023-03-…
$ servertimestamp                          <fct> 2023-03-23 20:18:53, 2023-03-…
$ firstactiontimestamp                     <fct> 2023-03-23 20:18:53, 2023-03-…
$ servertimepretty                         <fct> 21:18:53, 21:01:30, 19:56:41,…
$ serverdatepretty                         <fct> "Thursday, March 23, 2023", "…
$ serverdateprettyfirstaction              <fct> "Thursday, March 23, 2023", "…
$ servertimeprettyfirstaction              <fct> 21:18:53, 21:01:30, 19:56:41,…
$ visitortype                              <fct> new, new, new, returning, new…
$ visitcount                               <fct> 1, 1, 1, 4, 1, 3, 1, 3, 1, 2,…
$ dayssincefirstvisit                      <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ secondssincefirstvisit                   <fct> 0, 0, 0, 15464, 0, 10678, 0, …
$ visitduration                            <fct> 0, 20, 3, 1494, 0, 2555, 79, …
$ visitdurationpretty                      <fct> 0s, 20s, 3s, 24 min 54s, 0s, …
$ searches                                 <fct> 1, 0, 0, 7, 0, 2, 1, 0, 0, 9,…
$ actions                                  <fct> 1, 1, 1, 158, 1, 12, 9, 1, 1,…
$ interactions                             <fct> 1, 1, 1, 38, 1, 7, 5, 0, 1, 5…
$ referrertype                             <fct> website, website, website, di…
$ referrertypename                         <fct> Websites, Websites, Websites,…
$ referrername                             <fct> elearning.ohmportal.de, elear…
$ referrerurl                              <fct> https://elearning.ohmportal.d…
$ referrersearchengineurl                  <fct> NA, NA, NA, NA, NA, NA, NA, N…
$ languagecode                             <fct> de-de, de, de-de, en-us, de-d…
$ language                                 <fct> Language code de-de, German, …
$ devicetype                               <fct> Desktop, Desktop, Desktop, De…
$ devicebrand                              <fct> Apple, Unknown, Unknown, Appl…
$ devicemodel                              <fct> Generic Desktop, Generic Desk…
$ operatingsystem                          <fct> Mac 10.15, Windows 11, Window…
$ operatingsystemname                      <fct> Mac, Windows, Windows, Mac, i…
$ operatingsystemcode                      <fct> MAC, WIN, WIN, MAC, IOS, MAC,…
$ operatingsystemversion                   <fct> 10.15, 11, 10, 10.15, 16.3, 1…
$ browserfamily                            <fct> WebKit, Blink, Blink, Gecko, …
$ browserfamilydescription                 <fct> "WebKit (Safari)", "Blink (Ch…
$ browser                                  <fct> Safari 16.3, Microsoft Edge 1…
$ browsername                              <fct> Safari, Microsoft Edge, Chrom…
$ browsercode                              <fct> SF, PS, CH, FF, MF, FF, PS, F…
$ browserversion                           <fct> 16.3, 111, 111, 111, 16.3, 11…
$ events                                   <fct> 0, 0, 0, 120, 0, 5, 4, 1, 0, …
$ continent                                <fct> Europe, Europe, Europe, North…
$ continentcode                            <fct> eur, eur, eur, amn, eur, amn,…
$ country                                  <fct> Germany, Germany, Germany, Un…
$ countrycode                              <fct> de, de, de, us, de, us, de, u…
$ countryflag                              <fct> plugins/Morpheus/icons/dist/f…
$ region                                   <fct> NA, NA, NA, NA, NA, NA, NA, N…
$ regioncode                               <fct> NA, NA, NA, NA, NA, NA, NA, N…
$ city                                     <fct> NA, NA, NA, NA, NA, NA, NA, N…
$ location                                 <fct> "Germany", "Germany", "German…
$ latitude                                 <fct> NA, NA, NA, NA, NA, NA, NA, N…
$ longitude                                <fct> NA, NA, NA, NA, NA, NA, NA, N…
$ visitlocaltime                           <fct> 21:18:53, 21:01:29, 19:56:40,…
$ visitlocalhour                           <fct> 21, 21, 19, 18, 18, 17, 17, 1…
$ dayssincelastvisit                       <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ secondssincelastvisit                    <fct> 0, 0, 0, 4786, 0, 7331, 0, 73…
$ resolution                               <fct> 810x1080, 1536x864, 1920x1080…
$ plugins                                  <fct> "cookie", "cookie, pdf", "coo…
$ provider                                 <fct> NA, NA, NA, NA, NA, NA, NA, N…
$ providername                             <fct> "Unknown", "Unknown", "Unknow…
$ providerurl                              <fct> NA, NA, NA, NA, NA, NA, NA, N…
$ actiondetails_0_pageloadtime             <fct> NA, 2.23s, 0.55s, NA, 1s, NA,…
$ actiondetails_0_timespent                <fct> NA, 20, 0, 3, 0, 25, 4, NA, 0…
$ actiondetails_0_timespentpretty          <fct> NA, 20s, 0s, 3s, 0s, 25s, 4s,…
$ actiondetails_0_pageloadtimemilliseconds <fct> NA, 2233, 550, NA, 1001, NA, …
$ actiondetails_1_type                     <fct> NA, NA, NA, event, NA, event,…
$ actiondetails_1_url                      <fct> NA, NA, NA, "https://hans.th-…
$ actiondetails_1_pageidaction             <fct> NA, NA, NA, 10, NA, 10, 4, NA…
$ actiondetails_1_idpageview               <fct> NA, NA, NA, w2vQcb, NA, ZZd1q…
$ actiondetails_1_servertimepretty         <fct> NA, NA, NA, "Mar 23, 2023 18:…
$ actiondetails_1_pageid                   <fct> NA, NA, NA, 165, NA, 147, 143…
$ actiondetails_1_eventcategory            <fct> NA, NA, NA, click_button, NA,…
$ actiondetails_1_eventaction              <fct> NA, NA, NA, "Medien", NA, "ET…
$ actiondetails_1_pageviewposition         <fct> NA, NA, NA, 1, NA, 1, 1, NA, …
$ actiondetails_1_timestamp                <fct> NA, NA, NA, 2023-03-23 18:37:…
$ actiondetails_1_title                    <fct> NA, NA, NA, Event, NA, Event,…
$ actiondetails_1_subtitle                 <fct> NA, NA, NA, "Category: \"\"cl…
$ actiondetails_2_type                     <fct> NA, NA, NA, action, NA, searc…
$ actiondetails_2_url                      <fct> NA, NA, NA, "https://hans.th-…
$ actiondetails_2_pageidaction             <fct> NA, NA, NA, 9, NA, NA, 2, NA,…
$ actiondetails_2_idpageview               <fct> NA, NA, NA, EGozRT, NA, ZZd1q…
$ actiondetails_2_servertimepretty         <fct> NA, NA, NA, "Mar 23, 2023 18:…

4.1.5 Datensatz data_slim_filtered, Zeilen 1-100

tar_load(data_slim_filtered)

data_slim_filtered %>% 
  slice(1:100) |> 
  gt()

nr	type	value	idvisit
0	subtitle	https://hans.th-nuernberg.de/login	1
0	subtitle	https://hans.th-nuernberg.de/login?evalId=none&user=undefined&role=undefined	1
0	subtitle	https://hans.th-nuernberg.de/	1
0	subtitle	https://hans.th-nuernberg.de/login?evalId=none&role=undefined	1
0	timestamp	2023-03-23 14:20:12	1
0	timestamp	2024-06-06 11:19:16	1
0	timestamp	2022-12-05 15:33:45	1
0	timestamp	2023-10-04 16:19:46	1
1	eventcategory	click_videocard	1
1	eventcategory	login	1
1	eventaction	Einführung in HAnS	1
1	eventaction	success	1
1	timestamp	2023-03-23 14:20:26	1
1	timestamp	2024-06-06 11:21:07	1
1	timestamp	2022-12-05 15:33:49	1
1	timestamp	2023-10-04 16:19:54	1
1	subtitle	https://hans.th-nuernberg.de/	1
1	subtitle	https://hans.th-nuernberg.de/login?evalId=none&user=undefined&role=undefined	1
1	subtitle	Category: ""click_videocard', Action: ""Einführung in HAnS""	1
1	subtitle	Category: ""login', Action: ""success""	1
2	subtitle	Category: ""click_toggle', Action: ""Evaluation""	1
2	subtitle	https://hans.th-nuernberg.de/video-player?uuid=b57c1dfe-a667-48a6-b43d-dac7e600ae8c	1
2	subtitle	https://hans.th-nuernberg.de/?evalId=none&role=developer	1
2	timestamp	2023-03-23 14:20:28	1
2	timestamp	2022-12-05 15:33:49	1
2	timestamp	2023-10-04 16:19:54	1
2	eventcategory	click_toggle	1
2	eventaction	Evaluation	1
3	eventcategory	click_button	1
3	eventcategory	videoplayer_click	1
3	eventcategory	click_button	1
3	eventaction	Kanäle	1
3	eventaction	play	1
3	eventaction	Kanäle	1
3	timestamp	2023-03-23 14:20:30	1
3	timestamp	2022-12-05 15:33:49	1
3	timestamp	2023-10-04 16:19:56	1
3	subtitle	Category: ""click_button', Action: ""Kanäle""	1
3	subtitle	Category: ""videoplayer_click', Action: ""play""	1
3	subtitle	Category: ""click_button', Action: ""Kanäle""	1
4	subtitle	https://hans.th-nuernberg.de/channels	1
4	subtitle	https://hans.th-nuernberg.de/	1
4	subtitle	https://hans.th-nuernberg.de/channels?evalId=none&role=developer	1
4	timestamp	2023-03-23 14:20:30	1
4	timestamp	2022-12-05 15:33:52	1
4	timestamp	2023-10-04 16:19:56	1
5	eventcategory	click_button	1
5	eventcategory	click_button	1
5	eventaction	Medien	1
5	eventaction	Medien	1
5	timestamp	2023-03-23 14:20:31	1
5	timestamp	2022-12-05 15:33:52	1
5	timestamp	2023-10-04 16:21:23	1
5	subtitle	https://hans.th-nuernberg.de/	1
5	subtitle	Category: ""click_button', Action: ""Medien""	1
5	subtitle	Category: ""click_button', Action: ""Medien""	1
5	pagetitle	HAnS	1
6	eventcategory	click_button	1
6	eventaction	Medien	1
6	timestamp	2023-03-23 14:20:31	1
6	timestamp	2022-12-05 15:55:43	1
6	timestamp	2023-10-04 16:21:23	1
6	subtitle	Category: ""click_button', Action: ""Medien""	1
6	subtitle	https://hans.th-nuernberg.de/	1
6	subtitle	https://hans.th-nuernberg.de/?evalId=none&role=developer	1
6	pagetitle	HAnS	1
6	pagetitle	HAnS	1
7	pagetitle	HAnS	1
7	subtitle	Category: ""click_toggle', Action: ""Evaluation""	1
7	subtitle	https://hans.th-nuernberg.de/	1
7	subtitle	Category: ""click_button', Action: ""Medien""	1
7	timestamp	2023-03-23 14:20:34	1
7	timestamp	2022-12-05 16:03:01	1
7	timestamp	2023-10-04 16:25:22	1
7	eventcategory	click_toggle	1
7	eventcategory	click_button	1
7	eventaction	Evaluation	1
7	eventaction	Medien	1
8	subtitle	https://hans.th-nuernberg.de/	1
8	subtitle	https://hans.th-nuernberg.de/	1
8	subtitle	Category: ""click_button', Action: ""Kanäle""	1
8	timestamp	2023-03-23 14:21:47	1
8	timestamp	2022-12-05 16:03:06	1
8	timestamp	2023-10-04 16:25:23	1
8	eventcategory	click_button	1
8	eventaction	Kanäle	1
9	eventcategory	click_toggle	1
9	eventaction	Evaluation	1
9	timestamp	2023-03-23 14:22:41	1
9	timestamp	2022-12-05 16:03:16	1
9	timestamp	2023-10-04 16:25:23	1
9	subtitle	Category: ""click_toggle', Action: ""Evaluation""	1
9	subtitle	https://hans.th-nuernberg.de/video-player?uuid=b57c1dfe-a667-48a6-b43d-dac7e600ae8c	1
9	subtitle	https://hans.th-nuernberg.de/channels?evalId=none&role=developer	1
10	eventcategory	videoplayer_click	1
10	eventaction	play	1
10	timestamp	2023-03-23 14:22:45	1
10	timestamp	2022-12-05 16:03:16	1
10	timestamp	2023-10-04 16:25:24	1
10	subtitle	https://hans.th-nuernberg.de/channels	1

5 Datensatz nur User

Entfernt man Developer, Admins und Lecturers aus dem Roh-Datensatz so bleiben weniger Zeilen übrig:

tar_load(data_users_only)

• 11383 Zeilen
• 8399 Spalten

6 Datensatz mit Anzahl der Aktionen pro User

tar_load(count_action)

count_action |> 
  head(30) |> 
  kable()

idvisit	nr_max
1	90
2	5
3	29
4	340
5	265
6	32
7	122
8	1
9	39
10	56
11	177
12	266
13	157
14	118
15	158
16	117
17	499
18	499
19	164
20	22
21	499
22	163
23	49
24	14
25	25
26	59
27	151
28	23
29	45
30	133

7 Zeitraum

7.1 Beginn/Ende der Daten

tar_load(config)

tar_load(time_minmax)

time_minmax |> 
  head(30) |> 
  kable()

idvisit	time_min	time_max
1	2022-12-05 15:33:45	2024-06-06 11:21:07
2	2022-12-05 15:35:45	2023-03-23 14:31:36
3	2024-03-04 10:25:00	2024-06-06 11:50:30
4	2022-12-05 16:20:05	2024-10-07 12:20:29
5	2022-12-05 16:34:34	2023-10-04 20:56:37
6	2022-12-05 16:42:53	2024-06-06 12:40:58
7	2022-12-05 17:06:08	2024-10-07 14:34:33
8	2024-03-04 13:33:11	2024-06-06 13:13:16
9	2022-12-05 22:30:01	2024-06-06 13:31:33
10	2023-03-23 17:17:36	2024-06-06 13:34:51
11	2022-12-06 08:09:01	2024-10-07 15:13:15
12	2022-12-06 09:26:13	2024-06-06 16:22:08
13	2022-12-06 10:10:10	2024-10-07 16:59:17
14	2022-12-06 12:04:24	2024-06-06 15:47:58
15	2022-12-06 13:10:12	2024-10-08 08:28:21
16	2024-06-06 16:30:32	2024-06-06 17:23:23
17	2022-12-06 13:59:27	2024-10-08 11:56:22
18	2022-12-06 14:35:57	2024-06-06 18:12:59
19	2022-12-06 14:39:14	2024-06-06 20:12:09
20	2022-12-06 16:17:59	2024-06-06 21:22:11
21	2022-12-06 16:31:52	2024-10-08 14:40:45
22	2022-12-06 17:06:34	2024-10-08 15:54:44
23	2022-12-06 17:26:27	2024-06-07 09:04:56
24	2022-12-06 17:33:51	2024-10-08 15:55:58
25	2023-03-24 09:59:37	2024-06-07 10:42:23
26	2023-03-24 10:32:49	2024-06-07 10:42:53
27	2022-12-06 20:38:32	2024-06-07 13:49:34
28	2022-12-06 20:53:53	2024-06-07 11:52:27
29	2022-12-06 22:39:05	2024-06-07 13:40:09
30	2022-12-07 08:35:31	2024-10-08 18:00:01

time_minmax |> 
  summarise(time_min = min(time_min, na.rm = T),
            time_max = max(time_max, na.rm = T)) |> 
  gt()

time_min	time_max
2022-12-05 15:33:45	2025-03-03 14:08:54

Diese Statistik wurde auf Basis des Datenobjekts data_slim_filtered berechnet, vgl. das Target dieses Objekts in der Pipeline.

7.2 Days since last visit

tar_load(time_visit_wday)
tar_load(time_since_last_visit)

time_visit_wday |> 
  head(30) |> 
  kable()

idvisit	dow	hour	date_time
1	4	14	2023-03-23 14:20:12
2	4	14	2023-03-23 14:29:42
3	1	10	2024-03-04 10:25:00
4	4	15	2023-03-23 15:15:59
5	1	16	2022-12-05 16:34:34
6	4	12	2024-06-06 12:05:49
7	1	17	2022-12-05 17:06:08
8	1	13	2024-03-04 13:33:11
9	4	13	2024-06-06 13:30:26
10	4	17	2023-03-23 17:17:36
11	4	17	2023-03-23 17:18:10
12	1	16	2024-03-04 16:47:24
13	4	18	2023-03-23 18:37:56
14	4	15	2024-06-06 15:47:37
15	4	15	2024-06-06 15:57:14
16	4	16	2024-06-06 16:30:32
17	4	17	2024-06-06 17:31:43
18	5	9	2023-03-24 09:23:24
19	4	20	2024-06-06 20:12:04
20	5	9	2023-03-24 09:33:57
21	5	7	2024-06-07 07:56:34
22	2	8	2024-03-05 08:31:43
23	5	9	2023-03-24 09:57:44
24	5	9	2023-03-24 09:57:56
25	5	9	2023-03-24 09:59:37
26	5	10	2023-03-24 10:32:49
27	5	11	2024-06-07 11:09:46
28	5	10	2023-03-24 10:39:07
29	5	11	2023-03-24 11:11:53
30	3	8	2022-12-07 08:35:31

time_since_last_visit <- 
time_since_last_visit |> 
  mutate(dayssincelastvisit = as.numeric(dayssincelastvisit)) 

time_since_last_visit |> 
  datawizard::describe_distribution(dayssincelastvisit) |> 
  knitr::kable(digits = 2)

Variable	Mean	SD	IQR	Min	Max	Skewness	Kurtosis	n	n_Missing
dayssincelastvisit	6.3	15.38	0	1	92	3.32	10.84	18924	162

time_since_last_visit |>
  ggplot(aes(x=dayssincelastvisit)) +
  geom_density()

7.3 Visits im Zeitverlauf

Wie viele Visits (von Hans) gab es?

7.3.1 Pro Monat

time_visit_wday_summary <- 
time_visit_wday |> 
  ungroup() |> 
  mutate(month_start = floor_date(date_time, "month")) |> 
  mutate(month_name = month(date_time, label = TRUE, abbr = FALSE),
         month_num = month(date_time, label = FALSE),
         year_num = year(date_time)) |> 
  group_by(year_num, month_num) |> 
  summarise(n = n())

time_visit_wday_summary |> 
  gt()

month_num	n
2022
12	25
2023
1	20
2	77
3	99
4	219
5	300
6	246
7	390
8	20
9	27
10	22
11	8
12	98
2024
1	552
2	71
3	51
4	94
5	303
6	200
7	192
8	6
9	5
10	8
11	149
12	650
2025
1	830
2	131
3	2

time_visit_wday_summary2 <- 
time_visit_wday |> 
  ungroup() |> 
  mutate(month_start = floor_date(date_time, "month")) |> 
  mutate(month_name = month(date_time, label = TRUE, abbr = FALSE),
         month_num = month(date_time, label = FALSE),
         year_num = year(date_time)) |> 
  group_by(year_num, month_start) |> 
  summarise(n = n())


time_visit_wday_summary2 |> 
  ggplot(aes(x = month_start, y = n)) +
  geom_line(group = 1, color = "grey60") +
  geom_point() 

7.3.2 Akkumulierte Seitenaufrufe im Zeitverlauf

time_visit_wday_summary2 <- 
time_visit_wday |> 
  ungroup() |> 
  mutate(month_start = floor_date(date_time, "month")) |> 
  mutate(month_name = month(date_time, label = TRUE, abbr = FALSE),
         month_num = month(date_time, label = FALSE),
         year_num = year(date_time)) |> 
  group_by(year_num, month_start) |> 
  summarise(n = n()) |> 
  ungroup() |> 
  mutate(n_cumsum = cumsum(n))


time_visit_wday_summary2 |> 
  ggplot(aes(x = month_start, y = n_cumsum)) +
  geom_line(group = 1, color = "grey60") +
  geom_point() 

7.4 Statistiken

Die folgenden Statistiken beruhen auf dem Datensatz data_slim_filtered:

glimpse(data_slim_filtered)

Rows: 3,597,119
Columns: 4
$ nr      <int> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2,…
$ type    <fct> subtitle, subtitle, subtitle, subtitle, timestamp, timestamp, …
$ value   <chr> "https://hans.th-nuernberg.de/login", "https://hans.th-nuernbe…
$ idvisit <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…

nr fasst die Nummer der Aktion innerhalb eines bestimmten Visits.

7.5 Mit allen Daten (den 499er-Daten)

tbl_count_action <- 
count_action |> 
  describe_distribution(nr_max) |> 
  gt() |> 
  fmt_number(columns = where(is.numeric),
             decimals = 2)

tbl_count_action

Variable	Mean	SD	IQR	Min	Max	Skewness	Kurtosis	n	n_Missing
nr_max	129.35	152.20	161.00	1.00	499.00	1.40	0.77	4,795.00	0.00

gtsave(tbl_count_action, filename = "reports/tbl_count_action.png")

nr_max gibt den Maximalwert von nr zurück, sagt also, wie viele Aktionen maximal von einem Visitor ausgeführt wurden.

Betrachtet man die Anzahl der Aktionen pro Visitor näher, so fällt auf, dass der Maximalwert (499) sehr häufig vorkommt:

count_action |> 
  count(nr_max) |> 
  ggplot(aes(x = nr_max, y = n)) +
  geom_col()

Hier noch in einer anderen Darstellung:

count_action |> 
  count(nr_max) |> 
  ggplot(aes(x = nr_max, y = n)) +
  geom_point()

Der Maximalwert ist einfach auffällig häufig:

count_action |> 
  count(nr_max == 499) |> 
  gt()

nr_max == 499	n
FALSE	4386
TRUE	409

Es erscheint plausibel, dass der Maximalwert alle “gekappten” (zensierten, abgeschnittenen) Werte fasst, also viele Werte, die eigentlich größer wären (aber dann zensiert wurden).

7.6 Nur Visitors, für die weniger als 500 Aktionen protokolliert sind

count_action2 <- 
count_action |> 
  filter(nr_max != 499) 

count_action2 |> 
  describe_distribution(nr_max) |> 
  gt() |> 
  fmt_number(columns = where(is.numeric),
             decimals = 2)

Variable	Mean	SD	IQR	Min	Max	Skewness	Kurtosis	n	n_Missing
nr_max	94.89	106.74	122.00	1.00	496.00	1.56	2.01	4,386.00	0.00

7.7 Verteilung

7.8 Mit den 499er-Daten

count_action_avg = mean(count_action$nr_max)
count_action_sd = sd(count_action$nr_max)

count_action |> 
  ggplot() +
  geom_histogram(aes(x = nr_max)) +
  labs(x = "Anzahl von Aktionen pro Visit",
       y = "n",
       caption = "Der vertikale Strich zeigt den Mittelwert; der horizontale die SD") +
  theme_minimal() +
  geom_vline(xintercept = count_action_avg,
             color = palette_okabe_ito()[1]) +
  geom_segment(x = count_action_avg-count_action_sd,
               y = 0,
               xend = count_action_avg + count_action_sd,
               yend = 0,
               color = palette_okabe_ito()[2],
               size = 2) +
  annotate("label", x = count_action_avg, y = 1500, label = "MW") +
  annotate("label", x = count_action_avg + count_action_sd, y = 0, label = "SD")

  #geom_label(aes(x = count_action_avg), y = 1, label = "Mean")

• Mittelwert der Aktionen pro Visit: 129.35.
• SD der Aktionen pro Visit: 152.2.

7.9 Ohne 499er-Daten

count_action_avg2 = mean(count_action2$nr_max)
count_action_sd2 = sd(count_action2$nr_max)

count_action2 |> 
  ggplot() +
  geom_histogram(aes(x = nr_max)) +
  labs(x = "Anzahl von Aktionen pro Visit",
       y = "n",
       title = "Verteilung der User-Aktionen pro Visit",
       caption = "Der vertikale Strich zeigt den Mittelwert; der horizontale die SD") +
  theme_minimal() +
  geom_vline(xintercept = count_action_avg2,
             color = palette_okabe_ito()[1]) +
  geom_segment(x = count_action_avg-count_action_sd2,
               y = 0,
               xend = count_action_avg2 + count_action_sd2,
               yend = 0,
               color = palette_okabe_ito()[2],
               size = 2) +
  annotate("label", x = count_action_avg2, y = 1500, label = "MW", vjust = "top") +
  annotate("label", x = count_action_avg2 + count_action_sd2, y = 0, label = "SD", vjust = "bottom")

  #geom_label(aes(x = count_action_avg), y = 1, label = "Mean")

• Mittelwert der Aktionen pro Visit: 94.89.
• SD der Aktionen pro Visit: 106.74.

8 Verweildauer pro Visit

8.1 Berechnungsgrundlage der Verweildauer

Die Verweildauer wurde berechnet als Differenz zwischen kleinstem und größtem Datumszeitwert (POSixct) eines Visits, vgl. [Funktion diff_time](https://github.com/sebastiansauer/hans/blob/main/funs/diff_time.R). Diese Variable heißttime_diffim Objekttime_spent`.

Dabei wird das Objekt data_slim_filtered herangezogen, vgl. die Definition es Targets “time_spent” in der Targets-Pipeline.

tar_load(data_slim_filtered)
head(data_slim_filtered, 20) |> kable()

nr	type	value	idvisit
0	subtitle	https://hans.th-nuernberg.de/login	1
0	subtitle	https://hans.th-nuernberg.de/login?evalId=none&user=undefined&role=undefined	1
0	subtitle	https://hans.th-nuernberg.de/	1
0	subtitle	https://hans.th-nuernberg.de/login?evalId=none&role=undefined	1
0	timestamp	2023-03-23 14:20:12	1
0	timestamp	2024-06-06 11:19:16	1
0	timestamp	2022-12-05 15:33:45	1
0	timestamp	2023-10-04 16:19:46	1
1	eventcategory	click_videocard	1
1	eventcategory	login	1
1	eventaction	Einführung in HAnS	1
1	eventaction	success	1
1	timestamp	2023-03-23 14:20:26	1
1	timestamp	2024-06-06 11:21:07	1
1	timestamp	2022-12-05 15:33:49	1
1	timestamp	2023-10-04 16:19:54	1
1	subtitle	https://hans.th-nuernberg.de/	1
1	subtitle	https://hans.th-nuernberg.de/login?evalId=none&user=undefined&role=undefined	1
1	subtitle	Category: ““click_videocard’, Action:”“Einführung in HAnS””	1
1	subtitle	Category: ““login’, Action:”“success””	1

8.2 Vorverarbeitung

Die Visit-Zeit wurde auf 600 Min. trunkiert/begrenzt.

tar_load(time_spent)
tar_load(time_duration)

time_spent |> 
  head(30) |> 
  kable()

nr	type	value	idvisit	time	time_diff
0	timestamp	2023-03-23 14:20:12	1	1679581212	47418442
0	timestamp	2024-06-06 11:19:16	1	1717672756	47418442
0	timestamp	2022-12-05 15:33:45	1	1670254425	47418442
0	timestamp	2023-10-04 16:19:46	1	1696436386	47418442
1	timestamp	2023-03-23 14:20:26	1	1679581226	47418442
1	timestamp	2024-06-06 11:21:07	1	1717672867	47418442
1	timestamp	2022-12-05 15:33:49	1	1670254429	47418442
1	timestamp	2023-10-04 16:19:54	1	1696436394	47418442
2	timestamp	2023-03-23 14:20:28	1	1679581228	47418442
2	timestamp	2022-12-05 15:33:49	1	1670254429	47418442
2	timestamp	2023-10-04 16:19:54	1	1696436394	47418442
3	timestamp	2023-03-23 14:20:30	1	1679581230	47418442
3	timestamp	2022-12-05 15:33:49	1	1670254429	47418442
3	timestamp	2023-10-04 16:19:56	1	1696436396	47418442
4	timestamp	2023-03-23 14:20:30	1	1679581230	47418442
4	timestamp	2022-12-05 15:33:52	1	1670254432	47418442
4	timestamp	2023-10-04 16:19:56	1	1696436396	47418442
5	timestamp	2023-03-23 14:20:31	1	1679581231	47418442
5	timestamp	2022-12-05 15:33:52	1	1670254432	47418442
5	timestamp	2023-10-04 16:21:23	1	1696436483	47418442
6	timestamp	2023-03-23 14:20:31	1	1679581231	47418442
6	timestamp	2022-12-05 15:55:43	1	1670255743	47418442
6	timestamp	2023-10-04 16:21:23	1	1696436483	47418442
7	timestamp	2023-03-23 14:20:34	1	1679581234	47418442
7	timestamp	2022-12-05 16:03:01	1	1670256181	47418442
7	timestamp	2023-10-04 16:25:22	1	1696436722	47418442
8	timestamp	2023-03-23 14:21:47	1	1679581307	47418442
8	timestamp	2022-12-05 16:03:06	1	1670256186	47418442
8	timestamp	2023-10-04 16:25:23	1	1696436723	47418442
9	timestamp	2023-03-23 14:22:41	1	1679581361	47418442

time_spent <- 
  time_spent |> 
  # compute time (t) in minutes (min):
  mutate(t_min = as.numeric(time_diff, units = "mins")) |> 
  filter(t_min < 600)

8.3 Verweildauer-Statistiken in Sekunden

Die Verweildauer ist dargestellt auf Grundlage oben dargestellter Berechnungsgrundlage.

time_spent |> 
  summarise(
    mean_time_diff = round(mean(time_diff), 2),
    sd_time_diff = sd(time_diff),
    min_time_diff = min(time_diff),
    max_time_diff = max(time_diff)
  ) |> 
  summarise(
    mean_time_diff_avg = mean(mean_time_diff),
    sd_time_diff_avg = mean(sd_time_diff, na.rm = TRUE),
    min_time_diff_avg = mean(min_time_diff),
    max_time_diff_avg = mean(max_time_diff)
  ) |> 
  gt() |> 
  fmt_number(columns = everything(),
             decimals = 2)

mean_time_diff_avg	sd_time_diff_avg	min_time_diff_avg	max_time_diff_avg
142.52	0.00	142.52	142.52

8.4 Verweildauer auf Basis der Variable visitduration

Alternativ zur Berechnung der Verweildauer steht eine Variable, visitduration zur Verfügung, die (offenbar) die Dauer des Visits misst bzw. messen soll.

Allerdings resultieren substanziell andere Werte, wenn man diese Variable heranzieht zur Berechnung der Verweildauer, vgl. Target time_duration in der Targets-Pipeline.

time_duration |> 
  head(30) |> 
  kable()

idvisit	visitduration_sec
17	0
16	20
15	3
13	1494
14	0
11	2555
10	79
12	0
9	5
4	5479
8	4
7	0
6	4
5	0
3	0
1	1054
2	115
38	2085
36	1614
37	335
34	3012
35	0
33	1457
32	4023
31	549
30	0
26	4679
29	36
28	820
27	0

time_duration |> 
  summarise(duration_sec_avg = mean(visitduration_sec, na.rm = TRUE))  |> 
  mutate(duration_min_avg = duration_sec_avg / 60) |> 
  kable()

duration_sec_avg	duration_min_avg
1289.434	21.49056

8.5 Verweildauer-Statistiken in Minuten

time_spent_summary <- 
time_spent |> 
  summarise(
    mean_t_min = mean(t_min),
    sd_t_min = sd(t_min),
    min_t_min = min(t_min),
    max_t_min = max(t_min)
  ) |> 
   summarise(
    mean_t_min_avg = mean(mean_t_min),
    sd_t_min_avg = mean(sd_t_min, na.rm = TRUE),
    min_t_min_avg = mean(min_t_min),
    max_t_min_avg = mean(max_t_min)
  )

time_spent_summary |> 
  gt() |> 
  fmt_number(columns = everything(),
             decimals = 2)

mean_t_min_avg	sd_t_min_avg	min_t_min_avg	max_t_min_avg
142.52	0.00	142.52	142.52

small_padding_theme <- ggpubr::ttheme(
  tbody.style = tbody_style(size = 8), # Smaller font size can help
  colnames.style = colnames_style(size = 9, face = "bold"),
  padding = unit(c(2, 2), "mm") # Reduce horizontal and vertical padding
)

ggpubr::ggtexttable(time_spent_summary,
                    rows = NULL,
                    theme = small_padding_theme)

8.6 Visualisierung der Verweildauer

8.6.1 bins=20

time_spent |> 
  ggplot(aes(x = t_min)) +
  geom_histogram() +
  scale_x_time() +
  theme_minimal() +
  labs(y = "n",
       x = "Verweildauer in HaNS pro Visit in Minuten")

8.6.2 bins=100

time_spent |> 
  ggplot(aes(x = t_min)) +
  geom_histogram(binwidth = 5) +
  theme_minimal() +
  labs(y = "n",
       x = "Verweildauer in Minuten",
       title = "Verweildauer in HaNS pro Visit",
       caption = "binwidth = 5 Min.")

8.6.3 Zeitdauer begrenzt auf 1-120 Min.

time_spent2 <- 
time_spent |> 
  filter(t_min > 1, t_min < 120) 

time_spent2 |> 
  ggplot(aes(x = t_min)) +
  geom_histogram(binwidth = 10) +
  theme_minimal() +
  labs(y = "n",
       x = "Verweildauer in HaNS pro Visit in Minuten",
       title = "Verweildauer begrenzt auf 1-120 Minuten",
       caption = "bindwidth = 10 Min.")

8.6.4 Veränderung der Verweildauer im Zeitverlauf

time_spent_by_month <-
  time_spent |> 
  mutate(date = ymd_hms(value)) |> 
  mutate(month_start = floor_date(date, "month")) |> 
  mutate(month_name = month(month_start, label = TRUE, abbr = FALSE),
         month_num = month(month_start, label = FALSE),
         year = year(month_start)) |> 
  group_by(month_num, year) |> 
  summarise(time_spent_month_avg = mean(t_min, na.rm = TRUE),
            time_spent_month_sd = sd(t_min, na.rm = TRUE)) |> 
  arrange(year, month_num)

time_spent_by_month |> 
  gt() |> 
  fmt_auto()

year	time_spent_month_avg	time_spent_month_sd
12
2,022	10	0
2,023	268.264	196.857
2,024	251.396	176.941
2
2,023	421	180.428
2,024	309.517	202.612
2,025	214.55	172.93
3
2,023	107.075	86.062
2,025	9	0
4
2,023	379.868	230.16
2,024	112	0
7
2,023	187.708	160.938
2,024	566.692	50.806
8
2,023	129.368	105.653
2,024	582	0
9
2,023	414.391	208.662
10
2,023	181.716	115.475
2,024	43.284	12.632
1
2,024	170.763	141.968
2,025	336.1	199.91
5
2,024	268.909	130.843
6
2,024	336.303	182.84
11
2,024	374.266	209.407

time_spent_by_month |> 
  mutate(time_spent_month_avg = round(time_spent_month_avg, 2),
         time_spent_month_sd = round(time_spent_month_sd, 2)) |> 
  ggtexttable()

time_spent_by_month_name <- 
time_spent |> 
  mutate(date = ymd_hms(value)) |> 
  mutate(month_start = floor_date(date, "month")) |> 
  mutate(month_name = month(month_start, label = TRUE, abbr = FALSE),
         month_num = month(month_start, label = FALSE),
         year = year(month_start)) |> 
  group_by(month_start, year) |> 
  summarise(time_spent_month_avg = mean(t_min, na.rm = TRUE),
            time_spent_month_sd = sd(t_min, na.rm = TRUE))

time_spent_by_month_name |> 
ggplot(aes(x = month_start, y = time_spent_month_avg)) +
  geom_line(group = 1, color = "grey60") +
    geom_point() 

9 Was machen die User?

Was machen die Visitors eigentlich? Und wie oft?

9.1 Häufigkeiten

tar_load(count_action_type)

Für das Objekt count_action_type wurde die Spalte subtitle in den Langformat-Daten ausgewertet, s. Funktionsdefinition von count_user_action_type.

count_action_type |> 
  head(30)

   nr      type
1   0  subtitle
2   0  subtitle
3   0  subtitle
4   0  subtitle
5   0 timestamp
6   0 timestamp
7   0 timestamp
8   0 timestamp
9   1 timestamp
10  1 timestamp
11  1 timestamp
12  1 timestamp
13  1  subtitle
14  1  subtitle
15  1  subtitle
16  1  subtitle
17  2  subtitle
18  2  subtitle
19  2  subtitle
20  2 timestamp
21  2 timestamp
22  2 timestamp
23  3 timestamp
24  3 timestamp
25  3 timestamp
26  3  subtitle
27  3  subtitle
28  3  subtitle
29  4  subtitle
30  4  subtitle
                                                                                 value
1                                                   https://hans.th-nuernberg.de/login
2         https://hans.th-nuernberg.de/login?evalId=none&user=undefined&role=undefined
3                                                        https://hans.th-nuernberg.de/
4                        https://hans.th-nuernberg.de/login?evalId=none&role=undefined
5                                                                  2023-03-23 14:20:12
6                                                                  2024-06-06 11:19:16
7                                                                  2022-12-05 15:33:45
8                                                                  2023-10-04 16:19:46
9                                                                  2023-03-23 14:20:26
10                                                                 2024-06-06 11:21:07
11                                                                 2022-12-05 15:33:49
12                                                                 2023-10-04 16:19:54
13                                                       https://hans.th-nuernberg.de/
14        https://hans.th-nuernberg.de/login?evalId=none&user=undefined&role=undefined
15                               Category: click_videocard, Action: Einführung in HAnS
16                                                    Category: login, Action: success
17                                          Category: click_toggle, Action: Evaluation
18 https://hans.th-nuernberg.de/video-player?uuid=b57c1dfe-a667-48a6-b43d-dac7e600ae8c
19                            https://hans.th-nuernberg.de/?evalId=none&role=developer
20                                                                 2023-03-23 14:20:28
21                                                                 2022-12-05 15:33:49
22                                                                 2023-10-04 16:19:54
23                                                                 2023-03-23 14:20:30
24                                                                 2022-12-05 15:33:49
25                                                                 2023-10-04 16:19:56
26                                              Category: click_button, Action: Kanäle
27                                           Category: videoplayer_click, Action: play
28                                              Category: click_button, Action: Kanäle
29                                               https://hans.th-nuernberg.de/channels
30                                                       https://hans.th-nuernberg.de/
   idvisit   category
1        1 visit_page
2        1 visit_page
3        1 visit_page
4        1 visit_page
5        1       <NA>
6        1       <NA>
7        1       <NA>
8        1       <NA>
9        1       <NA>
10       1       <NA>
11       1       <NA>
12       1       <NA>
13       1 visit_page
14       1 visit_page
15       1      video
16       1      login
17       1 Evaluation
18       1 visit_page
19       1 visit_page
20       1       <NA>
21       1       <NA>
22       1       <NA>
23       1       <NA>
24       1       <NA>
25       1       <NA>
26       1     Kanäle
27       1      video
28       1     Kanäle
29       1 visit_page
30       1 visit_page

Achtung: Es kann sinnvoller sein, alternativ zu dieser Analyse die Analyse auf Basis von eventcategory heranzuziehen. Dort werden alle Arten von Events berücksichtigt. Hier, in der vorliegenden, nur ausgewählte Events.

9.1.1 Nach bestimmten Kategorien

count_action_type_counted <- 
count_action_type |> 
  drop_na() |> 
  count(category, sort = TRUE) |> 
  mutate(prop = round(n/sum(n), 2)) 

count_action_type_counted |> 
  gt()

category	n	prop
video	657274	0.83
click_slideChange	61934	0.08
visit_page	40549	0.05
Media item	11046	0.01
login	4148	0.01
in_media_search	3044	0.00
Search Results Count	2668	0.00
click_topic	2623	0.00
Medien	1359	0.00
logout	1283	0.00
Kanäle	1222	0.00
GESOA	971	0.00
click_channelcard	661	0.00
Evaluation	173	0.00
Data protection	35	0.00

9.1.2 Nach Kategorien im Zeitverlauf

count_action_type_per_month <- 
count_action_type |> 
  select(nr, idvisit, category) |> 
  ungroup() |> 
  left_join(time_visit_wday |> ungroup()) |> 
  select(-c(dow, hour, nr)) |> 
  drop_na() |> 
  mutate(month_start = floor_date(date_time, "month")) |> 
  count(month_start, category)

count_action_type_per_month |> 
  gt()

month_start	category	n
2022-12-01	Evaluation	2
2022-12-01	GESOA	6
2022-12-01	Kanäle	11
2022-12-01	Media item	208
2022-12-01	Medien	12
2022-12-01	Search Results Count	46
2022-12-01	click_channelcard	10
2022-12-01	click_slideChange	337
2022-12-01	click_topic	13
2022-12-01	in_media_search	9
2022-12-01	login	48
2022-12-01	logout	64
2022-12-01	video	2522
2022-12-01	visit_page	423
2023-01-01	GESOA	4
2023-01-01	Kanäle	2
2023-01-01	Medien	2
2023-01-01	Search Results Count	25
2023-01-01	click_channelcard	4
2023-01-01	click_slideChange	438
2023-01-01	click_topic	19
2023-01-01	in_media_search	7
2023-01-01	login	13
2023-01-01	logout	1
2023-01-01	video	3672
2023-01-01	visit_page	179
2023-02-01	Data protection	1
2023-02-01	Kanäle	7
2023-02-01	Medien	10
2023-02-01	Search Results Count	18
2023-02-01	click_slideChange	1823
2023-02-01	click_topic	3
2023-02-01	in_media_search	73
2023-02-01	login	38
2023-02-01	logout	10
2023-02-01	video	10606
2023-02-01	visit_page	491
2023-03-01	Data protection	3
2023-03-01	Evaluation	20
2023-03-01	GESOA	57
2023-03-01	Kanäle	218
2023-03-01	Media item	655
2023-03-01	Medien	215
2023-03-01	Search Results Count	215
2023-03-01	click_channelcard	102
2023-03-01	click_slideChange	1781
2023-03-01	click_topic	109
2023-03-01	in_media_search	86
2023-03-01	login	210
2023-03-01	logout	219
2023-03-01	video	9870
2023-03-01	visit_page	2394
2023-04-01	Data protection	2
2023-04-01	Evaluation	13
2023-04-01	GESOA	70
2023-04-01	Kanäle	181
2023-04-01	Media item	2796
2023-04-01	Medien	187
2023-04-01	Search Results Count	240
2023-04-01	click_channelcard	109
2023-04-01	click_slideChange	5428
2023-04-01	click_topic	203
2023-04-01	in_media_search	100
2023-04-01	login	364
2023-04-01	logout	251
2023-04-01	video	33235
2023-04-01	visit_page	3626
2023-05-01	Evaluation	24
2023-05-01	GESOA	193
2023-05-01	Kanäle	159
2023-05-01	Media item	228
2023-05-01	Medien	132
2023-05-01	Search Results Count	372
2023-05-01	click_channelcard	62
2023-05-01	click_slideChange	8371
2023-05-01	click_topic	445
2023-05-01	in_media_search	608
2023-05-01	login	320
2023-05-01	logout	91
2023-05-01	video	64184
2023-05-01	visit_page	4564
2023-06-01	Evaluation	14
2023-06-01	GESOA	100
2023-06-01	Kanäle	95
2023-06-01	Medien	60
2023-06-01	Search Results Count	337
2023-06-01	click_channelcard	52
2023-06-01	click_slideChange	10802
2023-06-01	click_topic	280
2023-06-01	in_media_search	536
2023-06-01	login	233
2023-06-01	logout	52
2023-06-01	video	70662
2023-06-01	visit_page	3486
2023-07-01	Data protection	5
2023-07-01	Evaluation	28
2023-07-01	GESOA	79
2023-07-01	Kanäle	147
2023-07-01	Media item	68
2023-07-01	Medien	106
2023-07-01	Search Results Count	264
2023-07-01	click_channelcard	68
2023-07-01	click_slideChange	6355
2023-07-01	click_topic	162
2023-07-01	in_media_search	624
2023-07-01	login	262
2023-07-01	logout	141
2023-07-01	video	68992
2023-07-01	visit_page	3784
2023-08-01	Evaluation	1
2023-08-01	GESOA	2
2023-08-01	Kanäle	10
2023-08-01	Medien	8
2023-08-01	Search Results Count	44
2023-08-01	click_channelcard	16
2023-08-01	click_slideChange	301
2023-08-01	click_topic	4
2023-08-01	in_media_search	1
2023-08-01	login	4
2023-08-01	logout	5
2023-08-01	video	2450
2023-08-01	visit_page	199
2023-09-01	Evaluation	5
2023-09-01	GESOA	4
2023-09-01	Kanäle	22
2023-09-01	Medien	10
2023-09-01	Search Results Count	41
2023-09-01	click_channelcard	19
2023-09-01	click_slideChange	109
2023-09-01	click_topic	15
2023-09-01	in_media_search	13
2023-09-01	login	6
2023-09-01	logout	9
2023-09-01	video	3292
2023-09-01	visit_page	267
2023-10-01	Kanäle	7
2023-10-01	Media item	310
2023-10-01	Medien	16
2023-10-01	Search Results Count	5
2023-10-01	click_slideChange	159
2023-10-01	click_topic	21
2023-10-01	in_media_search	5
2023-10-01	login	39
2023-10-01	logout	44
2023-10-01	video	1233
2023-10-01	visit_page	234
2023-11-01	Kanäle	3
2023-11-01	Media item	68
2023-11-01	Medien	1
2023-11-01	Search Results Count	2
2023-11-01	click_channelcard	8
2023-11-01	click_slideChange	98
2023-11-01	click_topic	1
2023-11-01	login	7
2023-11-01	logout	2
2023-11-01	video	1172
2023-11-01	visit_page	76
2023-12-01	GESOA	4
2023-12-01	Kanäle	3
2023-12-01	Medien	34
2023-12-01	Search Results Count	9
2023-12-01	click_channelcard	3
2023-12-01	click_slideChange	884
2023-12-01	click_topic	6
2023-12-01	in_media_search	6
2023-12-01	login	32
2023-12-01	logout	2
2023-12-01	video	8081
2023-12-01	visit_page	373
2024-01-01	Evaluation	23
2024-01-01	GESOA	60
2024-01-01	Kanäle	45
2024-01-01	Media item	144
2024-01-01	Medien	72
2024-01-01	Search Results Count	156
2024-01-01	click_channelcard	14
2024-01-01	click_slideChange	7781
2024-01-01	click_topic	106
2024-01-01	in_media_search	202
2024-01-01	login	317
2024-01-01	logout	92
2024-01-01	video	51149
2024-01-01	visit_page	2830
2024-02-01	Evaluation	1
2024-02-01	GESOA	3
2024-02-01	Kanäle	3
2024-02-01	Medien	3
2024-02-01	Search Results Count	37
2024-02-01	click_channelcard	1
2024-02-01	click_slideChange	786
2024-02-01	click_topic	8
2024-02-01	in_media_search	7
2024-02-01	login	60
2024-02-01	logout	16
2024-02-01	video	12419
2024-02-01	visit_page	448
2024-03-01	Data protection	1
2024-03-01	Evaluation	4
2024-03-01	GESOA	27
2024-03-01	Kanäle	12
2024-03-01	Media item	781
2024-03-01	Medien	21
2024-03-01	Search Results Count	70
2024-03-01	click_channelcard	13
2024-03-01	click_slideChange	526
2024-03-01	click_topic	78
2024-03-01	in_media_search	8
2024-03-01	login	94
2024-03-01	logout	20
2024-03-01	video	6388
2024-03-01	visit_page	924
2024-04-01	Data protection	2
2024-04-01	Evaluation	2
2024-04-01	GESOA	10
2024-04-01	Kanäle	20
2024-04-01	Medien	31
2024-04-01	Search Results Count	84
2024-04-01	click_channelcard	12
2024-04-01	click_slideChange	1727
2024-04-01	click_topic	140
2024-04-01	in_media_search	94
2024-04-01	login	104
2024-04-01	logout	29
2024-04-01	video	17945
2024-04-01	visit_page	1012
2024-05-01	Evaluation	3
2024-05-01	GESOA	20
2024-05-01	Kanäle	31
2024-05-01	Medien	39
2024-05-01	Search Results Count	187
2024-05-01	click_channelcard	16
2024-05-01	click_slideChange	3029
2024-05-01	click_topic	138
2024-05-01	in_media_search	114
2024-05-01	login	195
2024-05-01	logout	41
2024-05-01	video	39557
2024-05-01	visit_page	1987
2024-06-01	Data protection	6
2024-06-01	Evaluation	7
2024-06-01	GESOA	39
2024-06-01	Kanäle	62
2024-06-01	Media item	818
2024-06-01	Medien	84
2024-06-01	Search Results Count	174
2024-06-01	click_channelcard	47
2024-06-01	click_slideChange	4007
2024-06-01	click_topic	200
2024-06-01	in_media_search	109
2024-06-01	login	272
2024-06-01	logout	60
2024-06-01	video	31850
2024-06-01	visit_page	2390
2024-07-01	Data protection	1
2024-07-01	Evaluation	14
2024-07-01	GESOA	114
2024-07-01	Kanäle	52
2024-07-01	Medien	51
2024-07-01	Search Results Count	216
2024-07-01	click_channelcard	25
2024-07-01	click_slideChange	4700
2024-07-01	click_topic	177
2024-07-01	in_media_search	255
2024-07-01	login	216
2024-07-01	logout	48
2024-07-01	video	48145
2024-07-01	visit_page	2320
2024-08-01	Medien	1
2024-08-01	click_slideChange	2
2024-08-01	login	2
2024-08-01	video	184
2024-08-01	visit_page	29
2024-09-01	Data protection	1
2024-09-01	click_slideChange	3
2024-09-01	in_media_search	8
2024-09-01	login	4
2024-09-01	video	454
2024-09-01	visit_page	28
2024-10-01	GESOA	6
2024-10-01	click_slideChange	107
2024-10-01	login	10
2024-10-01	logout	2
2024-10-01	video	380
2024-10-01	visit_page	36
2024-11-01	Data protection	1
2024-11-01	GESOA	28
2024-11-01	Kanäle	10
2024-11-01	Medien	10
2024-11-01	Search Results Count	3
2024-11-01	click_channelcard	3
2024-11-01	click_slideChange	2380
2024-11-01	click_topic	40
2024-11-01	in_media_search	10
2024-11-01	login	127
2024-11-01	logout	8
2024-11-01	video	9909
2024-11-01	visit_page	658
2024-12-01	Data protection	10
2024-12-01	Evaluation	7
2024-12-01	GESOA	86
2024-12-01	Kanäle	65
2024-12-01	Media item	3099
2024-12-01	Medien	92
2024-12-01	Search Results Count	60
2024-12-01	click_channelcard	43
2024-12-01	click_topic	192
2024-12-01	in_media_search	80
2024-12-01	login	495
2024-12-01	logout	28
2024-12-01	video	61655
2024-12-01	visit_page	3057
2025-01-01	Data protection	2
2025-01-01	Evaluation	3
2025-01-01	GESOA	56
2025-01-01	Kanäle	40
2025-01-01	Media item	907
2025-01-01	Medien	146
2025-01-01	Search Results Count	51
2025-01-01	click_channelcard	23
2025-01-01	click_topic	255
2025-01-01	in_media_search	69
2025-01-01	login	568
2025-01-01	logout	42
2025-01-01	video	87736
2025-01-01	visit_page	4055
2025-02-01	Evaluation	2
2025-02-01	GESOA	3
2025-02-01	Kanäle	17
2025-02-01	Media item	610
2025-02-01	Medien	13
2025-02-01	Search Results Count	12
2025-02-01	click_channelcard	11
2025-02-01	click_topic	8
2025-02-01	in_media_search	20
2025-02-01	login	106
2025-02-01	logout	2
2025-02-01	video	9526
2025-02-01	visit_page	655
2025-03-01	Media item	354
2025-03-01	Medien	3
2025-03-01	login	2
2025-03-01	logout	4
2025-03-01	video	6
2025-03-01	visit_page	24

9.1.3 Nur die Top3-Kategorien

tar_load(time_visit_wday)

time_visit_wday |> 
  head(30) |> 
  kable()

idvisit	dow	hour	date_time
1	4	14	2023-03-23 14:20:12
2	4	14	2023-03-23 14:29:42
3	1	10	2024-03-04 10:25:00
4	4	15	2023-03-23 15:15:59
5	1	16	2022-12-05 16:34:34
6	4	12	2024-06-06 12:05:49
7	1	17	2022-12-05 17:06:08
8	1	13	2024-03-04 13:33:11
9	4	13	2024-06-06 13:30:26
10	4	17	2023-03-23 17:17:36
11	4	17	2023-03-23 17:18:10
12	1	16	2024-03-04 16:47:24
13	4	18	2023-03-23 18:37:56
14	4	15	2024-06-06 15:47:37
15	4	15	2024-06-06 15:57:14
16	4	16	2024-06-06 16:30:32
17	4	17	2024-06-06 17:31:43
18	5	9	2023-03-24 09:23:24
19	4	20	2024-06-06 20:12:04
20	5	9	2023-03-24 09:33:57
21	5	7	2024-06-07 07:56:34
22	2	8	2024-03-05 08:31:43
23	5	9	2023-03-24 09:57:44
24	5	9	2023-03-24 09:57:56
25	5	9	2023-03-24 09:59:37
26	5	10	2023-03-24 10:32:49
27	5	11	2024-06-07 11:09:46
28	5	10	2023-03-24 10:39:07
29	5	11	2023-03-24 11:11:53
30	3	8	2022-12-07 08:35:31

count_action_type_per_month_top3 <- 
count_action_type |> 
  select(nr, idvisit, category) |> 
  ungroup() |> 
  filter(category %in% c("video", "click_slideChange", "visit_page")) |> 
  left_join(time_visit_wday |> ungroup()) |> 
  select(-c(dow, hour, nr)) |> 
  drop_na() |> 
  mutate(month_start = floor_date(date_time, "month")) |> 
  count(month_start, category)

count_action_type_per_month_top3 |> 
  gt()

month_start	category	n
2022-12-01	click_slideChange	337
2022-12-01	video	2522
2022-12-01	visit_page	423
2023-01-01	click_slideChange	438
2023-01-01	video	3672
2023-01-01	visit_page	179
2023-02-01	click_slideChange	1823
2023-02-01	video	10606
2023-02-01	visit_page	491
2023-03-01	click_slideChange	1781
2023-03-01	video	9870
2023-03-01	visit_page	2394
2023-04-01	click_slideChange	5428
2023-04-01	video	33235
2023-04-01	visit_page	3626
2023-05-01	click_slideChange	8371
2023-05-01	video	64184
2023-05-01	visit_page	4564
2023-06-01	click_slideChange	10802
2023-06-01	video	70662
2023-06-01	visit_page	3486
2023-07-01	click_slideChange	6355
2023-07-01	video	68992
2023-07-01	visit_page	3784
2023-08-01	click_slideChange	301
2023-08-01	video	2450
2023-08-01	visit_page	199
2023-09-01	click_slideChange	109
2023-09-01	video	3292
2023-09-01	visit_page	267
2023-10-01	click_slideChange	159
2023-10-01	video	1233
2023-10-01	visit_page	234
2023-11-01	click_slideChange	98
2023-11-01	video	1172
2023-11-01	visit_page	76
2023-12-01	click_slideChange	884
2023-12-01	video	8081
2023-12-01	visit_page	373
2024-01-01	click_slideChange	7781
2024-01-01	video	51149
2024-01-01	visit_page	2830
2024-02-01	click_slideChange	786
2024-02-01	video	12419
2024-02-01	visit_page	448
2024-03-01	click_slideChange	526
2024-03-01	video	6388
2024-03-01	visit_page	924
2024-04-01	click_slideChange	1727
2024-04-01	video	17945
2024-04-01	visit_page	1012
2024-05-01	click_slideChange	3029
2024-05-01	video	39557
2024-05-01	visit_page	1987
2024-06-01	click_slideChange	4007
2024-06-01	video	31850
2024-06-01	visit_page	2390
2024-07-01	click_slideChange	4700
2024-07-01	video	48145
2024-07-01	visit_page	2320
2024-08-01	click_slideChange	2
2024-08-01	video	184
2024-08-01	visit_page	29
2024-09-01	click_slideChange	3
2024-09-01	video	454
2024-09-01	visit_page	28
2024-10-01	click_slideChange	107
2024-10-01	video	380
2024-10-01	visit_page	36
2024-11-01	click_slideChange	2380
2024-11-01	video	9909
2024-11-01	visit_page	658
2024-12-01	video	61655
2024-12-01	visit_page	3057
2025-01-01	video	87736
2025-01-01	visit_page	4055
2025-02-01	video	9526
2025-02-01	visit_page	655
2025-03-01	video	6
2025-03-01	visit_page	24

count_action_type_per_month_top3 |> 
  ggplot(aes(x = month_start, y = n, color = category, group = category)) +
  geom_line()

9.1.4 eventcategory

Für folgende Analyse wurde eine andere Variable als oben herangezogen, nämlich eventcategory. Dadurch resultieren etwas andere Ergebnisse.

data_slim_filtered_count <- 
data_slim_filtered |> 
  filter(type == "eventcategory") |> 
  count(value, sort = TRUE) 

data_slim_filtered_count |> 
  gt()

value	n
videoplayer_click	646885
clear_transcript_text_for_llm_context	104108
click_slideChange	61934
click_button	20409
click_transcript_word	7882
click_videocard	7164
verify_option_wrong	4255
login	4147
click_topic_position_using_image	2229
click_in_media_search_results	1268
in_media_search	1036
generate_questionaire	1014
generate_questionaire_interval_minutes	1014
click_start_resize	975
click_stop_resize	942
in_media_search_results	890
verify_option_correct	708
click_channelcard	587
click_videocard_search_lecturer	537
logout	519
select_transcript_text_for_llm_context	468
click_videocard_search_course_acronym	402
click_option	338
message_to_llm	279
message_to_llm_de	258
eval	240
llm_response_de	230
click_topic_position_using_link	213
press_enter	211
click_topic_details	181
click_toggle	171
click_videocard_search_semester	104
click_channelcard_search_course_acronym	80
userRole	75
click_videocard_search_course	57
click_channelcard_search_faculty_acronym	33
click_channelcard_search_lecturer	33
message_to_llm_en	27
llm_response_en	26
click_channelcard_search_course	20
click_channelcard_search_semester	10
click_channelcard_search_faculty	7
click_link	5
click_survey	3
click_chat_message_thumbs_down	1
click_chat_message_thumbs_up	1

data_slim_filtered_count |> 
  ggtexttable()

Als Excel-Datei abspeichern:

data_slim_filtered_count |> 
  writexl::write_xlsx(path = "obj/data_slim_filtered_count.xlsx")

9.2 Verteilung

9.2.1 Rohwerte

count_action_type |> 
  count(category, sort = TRUE) |> 
  ggplot(aes(y = reorder(category, n), x = n)) +
  geom_col() +
  geom_bar_text() +
  labs(
    x = "User-Aktion",
    y = "Aktion",
    title = "Anzahl der User-Aktionen nach Kategorie"
  ) +
  theme_minimal() +
  scale_x_continuous(labels = scales::comma)

9.2.2 Log-Skalierung

count_action_type |> 
  count(category, sort = TRUE) |> 
  ggplot(aes(y = reorder(category, n), x = n)) +
  geom_col() +
  geom_bar_text() +
  labs(
    x = "Anazhl der User-Aktionen",
    y = "Aktion",
    title = "Anzahl der User-Aktionen nach Kategorie",
    caption = "Log10-Skala"
  ) +
  theme_minimal() +
  scale_x_log10()

10 An welchen Tagen und zu welcher Zeit kommen die User zu HaNS?

10.1 Setup

# Define a vector with the names of the days of the week
# Note: Adjust the start of the week (Sunday or Monday) as per your requirement
days_of_week <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")

# Replace numbers with day names
time_visit_wday$dow2 <- factor(days_of_week[time_visit_wday$dow],
                               levels = days_of_week)

10.2 HaNS-Login nach Uhrzeit

time_visit_wday |> 
  as_tibble() |> 
  count(hour) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = hour, y = prop)) +
  geom_col() +
  theme_minimal() +
  labs(
    title = "HaNS-Nutzer sind keine Frühaufsteher",
    x = "Uhrzeit",
    y = "Anteil"
  )

 # coord_polar()

time_visit_wday |> 
  as_tibble() |> 
  count(hour) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = hour, y = prop)) +
  geom_col() +
  theme_minimal() +
  coord_polar()

10.3 Verteilung der HaNS-Besuche nach Wochentagen

time_visit_wday |> 
  as_tibble() |> 
  count(dow2) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = dow2, y = prop)) +
  geom_col() +
  theme_minimal() +
  labs(title = "Verteilung der HaNS-Logins nach Wochentagen",
       x = "Wochentag",
       y = "Anteil")

 # coord_polar()

time_visit_wday |> 
  as_tibble() |> 
  count(dow2) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = dow2, y = prop)) +
  geom_col() +
  theme_minimal() +
  labs(title = "Verteilung der HaNS-Logins nach Wochentagen",
       x = "Wochentag",
       y = "Anteil")  +
  coord_polar()

10.3.1 HaNS-Login nach Wochentagen Uhrzeit

time_visit_wday |> 
  as_tibble() |> 
  count(dow2, hour) |> 
  group_by(dow2) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = hour, y = prop)) +
  geom_col() +
  facet_wrap(~ dow2) +
  theme_minimal() +
  labs(title = "Verteilung der HaNS-Logins nach Wochentagen und Uhrzeiten",
       x = "Wochentag",
       y = "Anteil")

 # coord_polar()

time_visit_wday |> 
  as_tibble() |> 
  count(dow2, hour) |> 
  group_by(dow2) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = hour, y = prop)) +
  geom_col() +
  facet_wrap(~ dow2) +
  theme_minimal() +
  labs(title = "Verteilung der HaNS-Logins nach Wochentagen und Uhrzeiten",
       x = "Wochentag",
       y = "Anteil") +
  coord_polar()

10.4 Anzahl der Visits nach Datum (Tagen) und Uhrzeit (bin2d)

time2 <- 
time_visit_wday |> 
  ungroup() |> 
  mutate(date = as.Date(date_time))

time2 |> 
  ggplot(aes(x = date, y = hour)) +
  geom_bin2d(binwidth = c(1, 1)) + # (1 day, 1 hour)
  scale_x_date(date_breaks = "1 month") +
  theme(legend.position = "bottom") +
  scale_fill_viridis_c() +
  labs(caption = "Each x-bin maps to one week")

10.5 Anzahl der Visits nach Datum (Wochen) und Uhrzeit (bin2d)

time2 |> 
  ggplot(aes(x = date, y = hour)) +
  geom_bin2d(binwidth = c(7, 1)) +  # 1 week, 1 hour
  scale_x_date(date_breaks = "1 week", date_labels = "%W") +
  theme(legend.position = "bottom") +
  scale_fill_viridis_c()  +
  labs(x = "Week number in 2023/2024",
       caption = "Each x-bin maps to one week")

10.6 Anzahl der Visits nach Datum (Wochen) und Wochentag (bin2d)

time2 |> 
  ggplot(aes(x = date, y = dow)) +
  geom_bin2d(binwidth = c(7, 1)) +  # 1 week, 1 hour
  scale_x_date(date_breaks = "1 week", date_labels = "%W") +
  theme(legend.position = "bottom") +
  scale_fill_viridis_c()  +
  labs(x = "Week number in 2023/2024",
       caption = "Each x-bin maps to one week",
       y = "Day of Week") +
  scale_y_continuous(breaks = 1:7)

11 KI-Gebrauch

11.1 Interaktion mit dem LLM

Berechnungsgrundlage: Für diese Analyse wurden alle Events der Kategorie llm gefiltert.

11.1.1 Art und Anzahl der Interaktionen mit dem LLM

data_slim_filtered_ai <- 
data_slim_filtered |> 
  filter(type == "eventcategory") |> 
  filter(str_detect(value, "llm")) |> 
  count(value, sort = TRUE) |> 
  mutate(prop = n / sum(n))

data_slim_filtered_ai |> 
  gt() |> 
  fmt_auto()

value	n	prop
clear_transcript_text_for_llm_context	104,108	0.988
select_transcript_text_for_llm_context	468	0.004
message_to_llm	279	0.003
message_to_llm_de	258	0.002
llm_response_de	230	0.002
message_to_llm_en	27	2.562 × 10−4
llm_response_en	26	2.467 × 10−4

data_slim_filtered_ai |> 
  mutate(prop = round(prop, 3)) |> 
  ggtexttable()

11.1.2 Anteil Visitors, die mit dem LLM interagieren

data_slim_filtered_llm_interact <- 
data_slim_filtered |> 
  mutate(has_llm = str_detect(value, "llm"))  |> 
  group_by(idvisit) |> 
  summarise(llm_used_during_visit = any(has_llm == TRUE)) |> 
  count(llm_used_during_visit) |> 
  mutate(prop = round(n /sum(n), 2)) 

data_slim_filtered_llm_interact|> 
  gt()

llm_used_during_visit	n	prop
FALSE	4286	0.89
TRUE	509	0.11

data_slim_filtered_llm_interact |> 
  ggtexttable()

11.1.3 … Im Zeitverlauf

tar_load(idvisit_has_llm)

idvisit_has_llm |> 
  head(30) |> 
  kable()

nr	type	value	idvisit	uses_llm	date_time	dow	hour	year	month	year_month
0	timestamp	2022-12-05 15:33:45	1	FALSE	2022-12-05 15:33:45	2	15	2022	12	2022-12
0	timestamp	2022-12-05 15:35:45	2	FALSE	2022-12-05 15:35:45	2	15	2022	12	2022-12
0	timestamp	2024-03-04 10:25:00	3	TRUE	2024-03-04 10:25:00	2	10	2024	3	2024-3
0	timestamp	2022-12-05 16:20:05	4	FALSE	2022-12-05 16:20:05	2	16	2022	12	2022-12
0	timestamp	2022-12-05 16:34:34	5	FALSE	2022-12-05 16:34:34	2	16	2022	12	2022-12
0	timestamp	2022-12-05 16:42:53	6	FALSE	2022-12-05 16:42:53	2	16	2022	12	2022-12
0	timestamp	2022-12-05 17:06:08	7	FALSE	2022-12-05 17:06:08	2	17	2022	12	2022-12
0	timestamp	2024-03-04 13:33:11	8	FALSE	2024-03-04 13:33:11	2	13	2024	3	2024-3
0	timestamp	2022-12-05 22:30:01	9	FALSE	2022-12-05 22:30:01	2	22	2022	12	2022-12
0	timestamp	2023-03-23 17:17:36	10	FALSE	2023-03-23 17:17:36	5	17	2023	3	2023-3
0	timestamp	2022-12-06 08:09:01	11	FALSE	2022-12-06 08:09:01	3	8	2022	12	2022-12
0	timestamp	2022-12-06 09:26:13	12	FALSE	2022-12-06 09:26:13	3	9	2022	12	2022-12
0	timestamp	2022-12-06 10:10:10	13	FALSE	2022-12-06 10:10:10	3	10	2022	12	2022-12
0	timestamp	2022-12-06 12:04:24	14	FALSE	2022-12-06 12:04:24	3	12	2022	12	2022-12
0	timestamp	2022-12-06 13:10:12	15	FALSE	2022-12-06 13:10:12	3	13	2022	12	2022-12
0	timestamp	2024-06-06 16:30:32	16	FALSE	2024-06-06 16:30:32	5	16	2024	6	2024-6
0	timestamp	2022-12-06 13:59:27	17	TRUE	2022-12-06 13:59:27	3	13	2022	12	2022-12
0	timestamp	2022-12-06 14:35:57	18	TRUE	2022-12-06 14:35:57	3	14	2022	12	2022-12
0	timestamp	2022-12-06 14:39:14	19	FALSE	2022-12-06 14:39:14	3	14	2022	12	2022-12
0	timestamp	2022-12-06 16:17:59	20	FALSE	2022-12-06 16:17:59	3	16	2022	12	2022-12
0	timestamp	2022-12-06 16:31:52	21	TRUE	2022-12-06 16:31:52	3	16	2022	12	2022-12
0	timestamp	2022-12-06 17:06:34	22	FALSE	2022-12-06 17:06:34	3	17	2022	12	2022-12
0	timestamp	2022-12-06 17:26:27	23	FALSE	2022-12-06 17:26:27	3	17	2022	12	2022-12
0	timestamp	2022-12-06 17:33:51	24	FALSE	2022-12-06 17:33:51	3	17	2022	12	2022-12
1	timestamp	2022-12-06 17:33:51	24	FALSE	2022-12-06 17:33:51	3	17	2022	12	2022-12
0	timestamp	2023-03-24 09:59:37	25	FALSE	2023-03-24 09:59:37	6	9	2023	3	2023-3
0	timestamp	2023-03-24 10:32:49	26	FALSE	2023-03-24 10:32:49	6	10	2023	3	2023-3
0	timestamp	2022-12-06 20:38:32	27	FALSE	2022-12-06 20:38:32	3	20	2022	12	2022-12
0	timestamp	2022-12-06 20:53:53	28	FALSE	2022-12-06 20:53:53	3	20	2022	12	2022-12
0	timestamp	2022-12-06 22:39:05	29	TRUE	2022-12-06 22:39:05	3	22	2022	12	2022-12

idvisit_has_llm_timeline <- 
idvisit_has_llm |> 
  count(year_month, uses_llm) |> 
  ungroup() |> 
  group_by(year_month) |> 
  mutate(prop = round(n/sum(n), 2)) 

idvisit_has_llm_timeline|> 
  gt()

uses_llm	n	prop
2022-12
FALSE	275	0.78
TRUE	79	0.22
2023-1
FALSE	406	0.76
TRUE	126	0.24
2023-10
FALSE	96	0.78
TRUE	27	0.22
2023-11
FALSE	40	0.82
TRUE	9	0.18
2023-12
FALSE	314	0.99
TRUE	3	0.01
2023-2
FALSE	554	0.84
TRUE	108	0.16
2023-3
FALSE	74	0.94
TRUE	5	0.06
2023-4
FALSE	91	0.69
TRUE	40	0.31
2023-5
FALSE	119	0.74
TRUE	41	0.26
2023-6
FALSE	67	0.83
TRUE	14	0.17
2023-7
FALSE	219	0.93
TRUE	16	0.07
2023-8
FALSE	31	1.00
2023-9
FALSE	40	1.00
2024-1
FALSE	887	0.95
TRUE	44	0.05
2024-10
FALSE	9	1.00
2024-11
FALSE	166	0.92
TRUE	15	0.08
2024-12
FALSE	794	0.92
TRUE	67	0.08
2024-2
FALSE	94	1.00
2024-3
FALSE	6	0.67
TRUE	3	0.33
2024-4
FALSE	6	0.33
TRUE	12	0.67
2024-5
FALSE	113	1.00
2024-6
FALSE	70	0.91
TRUE	7	0.09
2024-7
FALSE	23	1.00
2024-8
FALSE	1	1.00
2025-1
FALSE	1008	0.94
TRUE	60	0.06
2025-2
FALSE	166	0.96
TRUE	7	0.04
2025-3
FALSE	2	1.00

idvisit_has_llm_timeline |> 
  ggtexttable()

idvisit_has_llm |> 
  count(year_month, uses_llm) |> 
  ungroup() |> 
  group_by(year_month) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = year_month, y = prop, color = uses_llm, groups = uses_llm)) +
  geom_point() +
  geom_line(aes(group = uses_llm)) +
  labs(title = "Visitors, die mit dem LLM interagieren im Zeitverlauf (Anteile)")

idvisit_has_llm |> 
  count(year_month, uses_llm) |> 
  ungroup() |> 
  group_by(year_month) |> 
  ggplot(aes(x = year_month, y = n, color = uses_llm, groups = uses_llm)) +
  geom_point() +
  geom_line(aes(group = uses_llm)) +
  labs(title = "Visitors, die mit dem LLM interagieren im Zeitverlauf (Anzahl)")

11.2 Klick auf ein Wort im Transkript

data_slim_filtered |> 
  filter(type == "subtitle") |> 
  filter(!is.na(value) & value != "") |> 
  count(click_transcript_word = str_detect(value, "click_transcript_word")) |> 
  mutate(prop = round(n/sum(n), 2)) |> 
  gt()

click_transcript_word	n	prop
FALSE	910840	0.99
TRUE	7882	0.01

11.3 KI-Aktionen

11.3.1 Insgesamt (ganzer Zeitraum)

tar_load(data_long)

data_long |> 
  head(30)

    idvisit             variable  value
     <char>               <char> <char>
 1:      13 actiondetails_0_type action
 2:      11 actiondetails_0_type action
 3:      10 actiondetails_0_type action
 4:       4 actiondetails_0_type  event
 5:       1 actiondetails_0_type action
 6:       2 actiondetails_0_type action
 7:      38 actiondetails_0_type  event
 8:      36 actiondetails_0_type  event
 9:      37 actiondetails_0_type  event
10:      34 actiondetails_0_type action
11:      33 actiondetails_0_type  event
12:      32 actiondetails_0_type  event
13:      31 actiondetails_0_type action
14:      26 actiondetails_0_type action
15:      29 actiondetails_0_type action
16:      28 actiondetails_0_type action
17:      18 actiondetails_0_type  event
18:      20 actiondetails_0_type action
19:      25 actiondetails_0_type action
20:      24 actiondetails_0_type action
21:      23 actiondetails_0_type  event
22:      44 actiondetails_0_type action
23:      45 actiondetails_0_type action
24:      43 actiondetails_0_type action
25:      42 actiondetails_0_type  event
26:      59 actiondetails_0_type  event
27:      57 actiondetails_0_type action
28:      52 actiondetails_0_type action
29:      51 actiondetails_0_type  event
30:      49 actiondetails_0_type action
    idvisit             variable  value

11.3.1.1 Im Detail

regex_pattern <-  "Category: \"(.*?)(?=', Action)"

# Explaining this regex_pattern:
# Find the literal string 
# 1. `Category: ` (surrounded by quotation marks)
# 2. Capture any characters (.*?) that follow, non-greedily, until...
# 3. ...it encounters the literal sequence,  ` Action`) immediately after the captured string.

ai_actions_count <- 
  data_long |> 
 # slice(1:1000) |> 
  filter(str_detect(value, "transcript")) |> 
  mutate(category = str_extract(value, regex_pattern)) |> 
  select(category) |> 
  mutate(category = str_replace_all(category, "[\"']", "")) |> 
  count(category, sort = TRUE) 

ai_actions_count |> 
  gt()

category	n
NA	217059
Category: clear_transcript_text_for_llm_context	104108
Category: click_transcript_word	7882
Category: select_transcript_text_for_llm_context	468
Category: click_button	20
Category: llm_response_de	3
Category: llm_response_en	2

11.3.2 KI-Klicks pro Monat

Im Objekt wird gezählt, wie oft der String "click_transcript_word" in den Daten (Langformat) gefunden wird, s. Target ai_transcript_clicks_per_month in der Targets-Pipeline.

tar_load(ai_transcript_clicks_per_month)

ai_transcript_clicks_per_month |> 
  head(30) |> 
  kable()

nr	type	value	idvisit	clicks_transcript_any	date_time	dow	hour	year	month	year_month
0	timestamp	2022-12-05 15:33:45	1	FALSE	2022-12-05 15:33:45	2	15	2022	12	2022-12
0	timestamp	2022-12-05 15:35:45	2	FALSE	2022-12-05 15:35:45	2	15	2022	12	2022-12
0	timestamp	2024-03-04 10:25:00	3	FALSE	2024-03-04 10:25:00	2	10	2024	3	2024-3
0	timestamp	2022-12-05 16:20:05	4	FALSE	2022-12-05 16:20:05	2	16	2022	12	2022-12
0	timestamp	2022-12-05 16:34:34	5	FALSE	2022-12-05 16:34:34	2	16	2022	12	2022-12
0	timestamp	2022-12-05 16:42:53	6	TRUE	2022-12-05 16:42:53	2	16	2022	12	2022-12
0	timestamp	2022-12-05 17:06:08	7	FALSE	2022-12-05 17:06:08	2	17	2022	12	2022-12
0	timestamp	2024-03-04 13:33:11	8	FALSE	2024-03-04 13:33:11	2	13	2024	3	2024-3
0	timestamp	2022-12-05 22:30:01	9	FALSE	2022-12-05 22:30:01	2	22	2022	12	2022-12
0	timestamp	2023-03-23 17:17:36	10	FALSE	2023-03-23 17:17:36	5	17	2023	3	2023-3
0	timestamp	2022-12-06 08:09:01	11	FALSE	2022-12-06 08:09:01	3	8	2022	12	2022-12
0	timestamp	2022-12-06 09:26:13	12	FALSE	2022-12-06 09:26:13	3	9	2022	12	2022-12
0	timestamp	2022-12-06 10:10:10	13	TRUE	2022-12-06 10:10:10	3	10	2022	12	2022-12
0	timestamp	2022-12-06 12:04:24	14	FALSE	2022-12-06 12:04:24	3	12	2022	12	2022-12
0	timestamp	2022-12-06 13:10:12	15	FALSE	2022-12-06 13:10:12	3	13	2022	12	2022-12
0	timestamp	2024-06-06 16:30:32	16	FALSE	2024-06-06 16:30:32	5	16	2024	6	2024-6
0	timestamp	2022-12-06 13:59:27	17	FALSE	2022-12-06 13:59:27	3	13	2022	12	2022-12
0	timestamp	2022-12-06 14:35:57	18	FALSE	2022-12-06 14:35:57	3	14	2022	12	2022-12
0	timestamp	2022-12-06 14:39:14	19	TRUE	2022-12-06 14:39:14	3	14	2022	12	2022-12
0	timestamp	2022-12-06 16:17:59	20	FALSE	2022-12-06 16:17:59	3	16	2022	12	2022-12
0	timestamp	2022-12-06 16:31:52	21	TRUE	2022-12-06 16:31:52	3	16	2022	12	2022-12
0	timestamp	2022-12-06 17:06:34	22	FALSE	2022-12-06 17:06:34	3	17	2022	12	2022-12
0	timestamp	2022-12-06 17:26:27	23	TRUE	2022-12-06 17:26:27	3	17	2022	12	2022-12
0	timestamp	2022-12-06 17:33:51	24	FALSE	2022-12-06 17:33:51	3	17	2022	12	2022-12
1	timestamp	2022-12-06 17:33:51	24	FALSE	2022-12-06 17:33:51	3	17	2022	12	2022-12
0	timestamp	2023-03-24 09:59:37	25	FALSE	2023-03-24 09:59:37	6	9	2023	3	2023-3
0	timestamp	2023-03-24 10:32:49	26	FALSE	2023-03-24 10:32:49	6	10	2023	3	2023-3
0	timestamp	2022-12-06 20:38:32	27	FALSE	2022-12-06 20:38:32	3	20	2022	12	2022-12
0	timestamp	2022-12-06 20:53:53	28	TRUE	2022-12-06 20:53:53	3	20	2022	12	2022-12
0	timestamp	2022-12-06 22:39:05	29	FALSE	2022-12-06 22:39:05	3	22	2022	12	2022-12

ai_transcript_clicks_per_month_count <-
ai_transcript_clicks_per_month |> 
  count(year_month, clicks_transcript_any) |> 
  ungroup() |> 
  group_by(year_month) |> 
  mutate(prop = round(n/sum(n), 2)) 

ai_transcript_clicks_per_month_count|> 
  gt()

clicks_transcript_any	n	prop
2022-12
FALSE	245	0.69
TRUE	109	0.31
2023-1
FALSE	303	0.57
TRUE	229	0.43
2023-10
FALSE	98	0.80
TRUE	25	0.20
2023-11
FALSE	41	0.84
TRUE	8	0.16
2023-12
FALSE	270	0.85
TRUE	47	0.15
2023-2
FALSE	479	0.72
TRUE	183	0.28
2023-3
FALSE	58	0.73
TRUE	21	0.27
2023-4
FALSE	93	0.71
TRUE	38	0.29
2023-5
FALSE	105	0.66
TRUE	55	0.34
2023-6
FALSE	61	0.75
TRUE	20	0.25
2023-7
FALSE	150	0.64
TRUE	85	0.36
2023-8
FALSE	27	0.87
TRUE	4	0.13
2023-9
FALSE	33	0.82
TRUE	7	0.17
2024-1
FALSE	765	0.82
TRUE	166	0.18
2024-10
FALSE	9	1.00
2024-11
FALSE	163	0.90
TRUE	18	0.10
2024-12
FALSE	778	0.90
TRUE	83	0.10
2024-2
FALSE	69	0.73
TRUE	25	0.27
2024-3
FALSE	5	0.56
TRUE	4	0.44
2024-4
FALSE	16	0.89
TRUE	2	0.11
2024-5
FALSE	106	0.94
TRUE	7	0.06
2024-6
FALSE	73	0.95
TRUE	4	0.05
2024-7
FALSE	17	0.74
TRUE	6	0.26
2024-8
FALSE	1	1.00
2025-1
FALSE	982	0.92
TRUE	86	0.08
2025-2
FALSE	171	0.99
TRUE	2	0.01
2025-3
FALSE	2	1.00

ai_transcript_clicks_per_month_count |> 
  ggtexttable()

ai_transcript_clicks_per_month_count |> 
  mutate(date = ymd(paste0(year_month,"-01"))) |> 
  ggplot(aes(x = date, y = n)) +
  geom_line(group = 1) +
  geom_point()

12 Videozeit

Wie viel Zeit verbringen die Nutzer mit dem Betrachten von Videos (“Glotzdauer”)?

12.1 Glotzdauer allgemein

Achtung: Die Videozeit ist schwierig auszuwerten. Die Nutzer beenden keine Videos, in dem sie auf “Pause” drücken, sondern indem sie andere Aktionen durchführen. Dies ist aber analytisch schwer abzubilden.

tar_load(glotzdauer)

Vgl. die Definition des Targets glotzdauer in der Pipeline.

Kurz gesagt wird die Zeit-Differenz zwischen zwei aufeinander folgenden “Play” und “Pause” Aktionen berechnet.

Allerdings hat dieses Vorgehen Schwierigkeiten: Nicht immer folgt auf einem “Play” ein “Pause”. Es ist schwer auszuwerten, wann die Betrachtung eines Videos endet. Daher ist diese Analyse nur vorsichtig zu interpretieren.

Die Definition der Funktion glotzdauer.R ist online dokumentiert.

glotzdauer |> 
  head(30) |> 
  kable()

idvisit	first_play	last_pause	date	time_diff
1	2023-03-23 14:22:45	2023-10-04 16:40:59	2023-03-23	16852694 secs
3	2024-06-06 11:36:18	2024-06-06 11:36:19	2024-06-06	1 secs
4	2023-03-23 15:15:59	2023-03-23 15:49:34	2023-03-23	2015 secs
5	2022-12-05 16:35:19	2022-12-05 16:35:22	2022-12-05	3 secs
6	2022-12-05 17:26:05	2022-12-05 17:26:06	2022-12-05	1 secs
7	2022-12-05 17:06:21	2022-12-05 17:06:08	2022-12-05	-13 secs
8	2024-03-04 13:33:11	2024-03-04 13:33:12	2024-03-04	1 secs
9	2022-12-05 22:33:03	2022-12-05 22:32:59	2022-12-05	-4 secs
10	2023-03-23 17:18:39	2023-03-23 17:17:40	2023-03-23	-59 secs
11	2022-12-06 08:14:28	2022-12-06 08:14:11	2022-12-06	-17 secs
12	2024-06-06 15:42:21	2024-06-06 15:42:25	2024-06-06	4 secs
13	2023-03-23 18:37:59	2023-03-23 18:38:15	2023-03-23	16 secs
14	2022-12-06 12:22:50	2022-12-06 12:21:49	2022-12-06	-61 secs
15	2022-12-06 13:12:47	2022-12-06 13:12:47	2022-12-06	0 secs
16	2024-06-06 16:30:51	2024-06-06 16:32:18	2024-06-06	87 secs
17	2024-06-06 17:32:08	2024-10-08 11:23:06	2024-06-06	10691458 secs
18	2023-03-24 09:30:16	2023-03-24 09:45:54	2023-03-24	938 secs
19	2022-12-06 14:41:45	2022-12-06 14:42:03	2022-12-06	18 secs
20	2022-12-06 16:55:16	2022-12-06 16:55:29	2022-12-06	13 secs
21	2024-06-07 07:57:02	2024-06-07 07:57:19	2024-06-07	17 secs
22	2022-12-06 17:08:50	2022-12-06 17:09:05	2022-12-06	15 secs
23	2024-03-05 09:31:13	2023-03-24 09:57:51	2023-03-24	-29979202 secs
24	2023-03-24 09:58:17	2023-03-24 09:57:56	2023-03-24	-21 secs
25	2023-03-24 10:00:03	2023-03-24 10:00:16	2023-03-24	13 secs
26	2023-03-24 10:52:58	NA	2023-03-24	NA secs
27	2024-06-07 11:13:24	2024-06-07 11:13:24	2024-06-07	0 secs
28	2022-12-06 21:11:54	2022-12-06 21:12:27	2022-12-06	33 secs
29	2023-03-24 11:12:21	2023-03-24 11:12:28	2023-03-24	7 secs
30	2022-12-07 08:35:31	2022-12-07 08:35:32	2022-12-07	1 secs
31	2023-03-24 13:26:00	2023-03-24 13:34:27	2023-03-24	507 secs

Für die folgende Darstellung wurden die absoluten Zeitwerte verwendet, d.h. ohne Vorzeichen.

glotzdauer |> 
  # we will assume that negative glotzdauer is the as positive glotzdauer:
  mutate(time_diff = abs(time_diff)) |> 
  # without glotzdauer smaller than 10 minutes:
  filter(time_diff < 60*10) |> 
  ggplot(aes(x = time_diff)) +
  geom_histogram() +
  scale_x_time() +
  labs(x = "Time interval [minutes]",
       caption = "Only time intervals less than 10 minutes. It is assumed that video time is positive only (no negative time intervals).")

glotzdauer_prepped <- 
glotzdauer |> 
  # we will assume that negative glotzdauer is the as positive glotzdauer:
  mutate(time_diff_abs_sec = abs(as.numeric(time_diff, units = "secs"))) |> 
  # without glotzdauer smaller than 10 minutes:
  filter(time_diff_abs_sec < 60*10) |> 
  mutate(time_diff_abs_min = time_diff_abs_sec / 60) 

glotzdauer_tbl <- 
  glotzdauer_prepped |> 
  select(time_diff_abs_sec, time_diff_abs_min) |> 
  describe_distribution()

glotzdauer_tbl |> 
  kable()

Variable	Mean	SD	IQR	Min	Max	Skewness	Kurtosis	n	n_Missing
time_diff_abs_sec	57.8050366	103.166468	55.0000000	0	597.00	2.74483	7.78949	3693	0
time_diff_abs_min	0.9634173	1.719441	0.9166667	0	9.95	2.74483	7.78949	3693	0

glotzdauer_tbl |>
  mutate(across(where(is.numeric), ~ round(., 2))) |>
  ggpubr::ggtexttable()

12.2 Glotzdauer im Zeitverlauf

glotzdauer_prepped_tbl <-
glotzdauer_prepped |> 
  mutate(first_of_month = floor_date(date, unit = "month")) |> 
  group_by(first_of_month) |> 
  summarise(time_diff_mean = mean(time_diff, na.rm = TRUE))
  

glotzdauer_prepped_tbl |> 
  kable()

first_of_month	time_diff_mean
2022-12-01	22.736364 secs
2023-01-01	28.109244 secs
2023-02-01	15.815451 secs
2023-03-01	47.571429 secs
2023-04-01	44.633588 secs
2023-05-01	46.980582 secs
2023-06-01	52.568750 secs
2023-07-01	35.906250 secs
2023-08-01	109.500000 secs
2023-09-01	23.733333 secs
2023-10-01	23.500000 secs
2023-11-01	27.833333 secs
2023-12-01	8.804196 secs
2024-01-01	46.468619 secs
2024-02-01	18.714286 secs
2024-03-01	16.774193 secs
2024-04-01	30.059701 secs
2024-05-01	43.555556 secs
2024-06-01	53.278846 secs
2024-07-01	55.058824 secs
2024-08-01	-24.000000 secs
2024-09-01	-9.000000 secs
2024-10-01	36.750000 secs
2024-11-01	47.181818 secs
2024-12-01	60.773707 secs
2025-01-01	52.115732 secs
2025-02-01	14.147059 secs
2025-03-01	5.000000 secs

glotzdauer_prepped_tbl |> 
  ggplot(aes(x = first_of_month, y = time_diff_mean)) +
  geom_line()